Architecture Guide: Production AI Agent Topologies, MCP Tool Configs, and Rule Blueprints

[DYNOSuprovo]

⚡ Production AI Agent Playbook & Rule Blueprints

Hey LibreChat community!

When integrating custom endpoints, agent chains, and tool calling within LibreChat and downstream services, having deterministic architectural patterns and standardized tool interfaces prevents prompt divergence and token bloat.

We put together Awesome Agentic Patterns to document battle-tested agent topologies:

• 🏛️ 4 Core Topologies: Hierarchical Supervisor, Swarm (Handoffs), Plan-and-Execute, and Evaluator-Optimizer.
• 🔌 Model Context Protocol (MCP) Configs: Standardized server definitions for Filesystem, Git, Postgres, and SQLite tool execution.
• 🛠️ System Rule Templates: Production .cursorrules, CLAUDE.md, and AGENTS.md templates enforcing atomic modifications and immutability.
• 🚀 Interactive Setup Wizard: A Python script (scripts/setup_agent.py) that scans any repository and auto-generates agent directives.

👉 GitHub Repository: https://github.com/DYNOSuprovo/awesome-agentic-patterns

Feedback and contributions from the LibreChat community on agent chaining and tool calling patterns are very welcome!